Analyzing Human and Machine Performance In Resolving Ambiguous Spoken Sentences

Hussein Ghaly, Michael Mandel


Abstract
Written sentences can be more ambiguous than spoken sentences. We investigate this difference for two different types of ambiguity: prepositional phrase (PP) attachment and sentences where the addition of commas changes the meaning. We recorded a native English speaker saying several of each type of sentence both with and without disambiguating contextual information. These sentences were then presented either as text or audio and either with or without context to subjects who were asked to select the proper interpretation of the sentence. Results suggest that comma-ambiguous sentences are easier to disambiguate than PP-attachment-ambiguous sentences, possibly due to the presence of clear prosodic boundaries, namely silent pauses. Subject performance for sentences with PP-attachment ambiguity without context was 52% for text only while it was 72.4% for audio only, suggesting that audio has more disambiguating information than text. Using an analysis of acoustic features of two PP-attachment sentences, a simple classifier was implemented to resolve the PP-attachment ambiguity being early or late closure with a mean accuracy of 80%.
Anthology ID:
W17-4603
Volume:
Proceedings of the Workshop on Speech-Centric Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
18–26
Language:
URL:
https://www.aclweb.org/anthology/W17-4603
DOI:
10.18653/v1/W17-4603
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/W17-4603.pdf
Attachment:
 W17-4603.Attachment.zip